Neuroscience · Life Sciences
Neurons & Cognition
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163 papers, from 31 August 2026 to 5 November 2026: 140 arXiv preprints and 23 OpenAlex records.
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Newest papers, page 2
A Low-Rank Space Captures Linear Alignment Between LLM and Human Neural Activity During Naturalistic Language Listening
Large language models (LLMs) are increasingly used as computational models of human language processing, yet it remains unclear which components of their representations drive alignment with neural…Data Amount Heterogeneity Improves Performance in Federated Learning for Neuroimaging
Federated Learning (FL) allows for secure analysis of distributed data without data sharing.REVBRAIN: Real-time Probabilistic EEG Forecasting Using Residual Vector Quantization
Real-time forecasting of resting-state EEG signals is required by applications like brain-state-dependent stimulation, where computer generated actions are coupled with ongoing neural oscillations.Stationary Action Principles for Neuronal Growth
Neuronal growth is guided by biochemical, mechanical, and geometrical cues while being influenced by stochastic fluctuations in cytoskeletal dynamics, adhesion, and growth cone sensing.CogArena: Benchmarking Multimodal Agents on Interactive Behavioral Experiments
Claims about the cognitive capabilities of large language models are typically tested by translating behavioral paradigms into text prompts, bypassing the visual interfaces and time pressure through…Neuronal Identity as an Organizational Basis for Analyzing Neural Population Dynamics
Individual neurons exhibit diverse molecular, morphological, and electrophysiological properties that shape their distinct roles in neural circuits.Every route counts, but some count more: a max-plus view of the C. elegans brain
Brains do not simply add up every signal they receive: they select which routes dominate.Solitary movement style predicts collective foraging while whom to follow emerges only in company
How far can behaviour inside a group, and the group's performance, be predicted from members measured while alone?A theory of noisy linear recurrent neural network learning dynamics
Biological neural networks are noisy: their dynamics involve difficult-to-predict fluctuations which are likely due not just to chaos, but also to a variety of low-level biophysical processes.On the stability of learning
Learning algorithms are usually analyzed under the assumption that informative gradients are continuously available.SOMA: A Benchmark for Fine-Grained Egocentric Behavior and Body-Motion Understanding
Perceiving human body motion is critical for multimodal systems that understand and assist people.Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila
Animals have a remarkable capacity for autonomy---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind this ability largely…Sensorimotor behaviour from an untrained brain-and-nerve-cord connectome
Connectomes now map the neurons and synapses of an adult fly's brain and nerve cord, but how much of behaviour follows from the wiring is unknown.Multitask Interactions in Recurrent Dynamics across 100 Cognitive Tasks
The brain continually learns tasks with very different computational demands.Virtual Placebo: a scoping review of control conditions in randomized controlled trials of psychological immersive virtual reality interventions
Research on immersive virtual reality (IVR) as a therapeutic tool is growing rapidly, with randomized controlled trials (RCTs) now commonplace.ZENO: Nonlinear filtering with Zakai Equation Neural Operators
State inference and parameter learning for nonlinear filters is a core computation for scientific machine learning with varied applications in engineering and neuroscience.Neural-DISCO: Source-Conditioned Counterfactual Editing of Neural Population Activity
Understanding how neural activity encodes behavioral and sensory variables is a key challenge in systems neuroscience.A Mathematical Theory of the Jacobian Lens
Recent work from Anthropic introduced a new interpretability tool, called the Jacobian lens (J-lens, Gurnee et al., 2026).Bio-Inspired Auditory Processing: Coupling Parallel Resonate-and-Fire Dynamics with Ternary Recurrent Networks for Speech Recognition
Spiking Neural Networks (SNNs) promise energy-efficient inference through sparse, event-driven computation, yet remain constrained on continuous, context-dependent tasks such as automatic speech…A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners
Gold's theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone.